The field of robotic manipulation and interaction is witnessing significant developments, with a focus on improving the efficiency and adaptability of robotic systems in complex environments. Researchers are exploring innovative approaches to address challenges such as partial observability, uncertain environments, and the need for precise guidance. A key direction is the development of probabilistic and learning-based methods that enable robots to adapt to changing conditions and uncertainties. Another area of research is the design of controllers that can balance exploratory and exploitative behaviors, allowing robots to efficiently interact with their environment. Noteworthy papers in this area include:

• Optimal Robotic Velcro Peeling with Force Feedback, which proposes a novel method for peeling a Velcro strap from a surface using a robotic manipulator with force feedback.
• Hearing the Slide: Acoustic-Guided Constraint Learning for Fast Non-Prehensile Transport, which presents a method for learning a friction model using acoustic sensing to improve object transport efficiency.
• A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures, which introduces a unified framework for probabilistic virtual fixtures that enables seamless switching between manual, semi-automated, and fully autonomous modes.
• RICE: Reactive Interaction Controller for Cluttered Canopy Environment, which proposes a reactive controller for safe navigation in dense, cluttered environments.